Вывод с помощью Gemma с использованием JAX и Flax

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Обзор

Gemma — это семейство легких современных моделей открытого большого языка, основанных на исследованиях и технологиях Google DeepMind Gemini. В этом руководстве показано, как выполнить базовую выборку/вывод с помощью модели Gemma 2B Instruct с использованием библиотеки gemma Google DeepMind , написанной с помощью JAX (библиотека высокопроизводительных численных вычислений), Flax (библиотека нейронных сетей на основе JAX), Orbax (библиотека высокопроизводительных численных вычислений). библиотека на основе JAX для обучения утилитам, таким как создание контрольных точек) и SentencePiece (библиотека токенизатора/детокенизатора). Хотя лен не используется непосредственно в этом блокноте, лен использовался для создания Джеммы.

Этот ноутбук может работать на Google Colab с бесплатным графическим процессором T4 (перейдите в «Правка» > «Настройки ноутбука» > в разделе «Аппаратный ускоритель» выберите «T4 GPU »).

Настраивать

1. Настройте доступ Kaggle для Джеммы.

Чтобы выполнить это руководство, сначала необходимо следовать инструкциям по установке на странице Gemma setup , в которых показано, как сделать следующее:

  • Получите доступ к Джемме на kaggle.com .
  • Выберите среду выполнения Colab с достаточными ресурсами для запуска модели Gemma.
  • Создайте и настройте имя пользователя Kaggle и ключ API.

После завершения настройки Gemma перейдите к следующему разделу, где вы установите переменные среды для вашей среды Colab.

2. Установите переменные среды

Установите переменные среды для KAGGLE_USERNAME и KAGGLE_KEY . При появлении запроса «Предоставить доступ?» сообщения, согласитесь предоставить секретный доступ.

import os
from google.colab import userdata # `userdata` is a Colab API.

os.environ["KAGGLE_USERNAME"] = userdata.get('KAGGLE_USERNAME')
os.environ["KAGGLE_KEY"] = userdata.get('KAGGLE_KEY')

3. Установите библиотеку gemma

В этом ноутбуке основное внимание уделяется использованию бесплатного графического процессора Colab. Чтобы включить аппаратное ускорение, нажмите «Редактировать» > «Настройки ноутбука» > «Выбрать графический процессор T4» > «Сохранить» .

Далее вам необходимо установить библиотеку gemma Google DeepMind с github.com/google-deepmind/gemma . Если вы получаете сообщение об ошибке «преобразователь зависимостей pip», обычно вы можете игнорировать его.

pip install -q git+https://github.com/google-deepmind/gemma.git

Загрузите и подготовьте модель Джеммы.

  1. Загрузите модель Gemma с помощью kagglehub.model_download , который принимает три аргумента:
  • handle : ручка модели от Kaggle.
  • path : (Необязательная строка) Локальный путь
  • force_download : (Необязательное логическое значение) Принудительно повторно загрузить модель.
GEMMA_VARIANT = 'gemma2-2b-it' # @param ['gemma2-2b', 'gemma2-2b-it'] {type:"string"}

import kagglehub

GEMMA_PATH = kagglehub.model_download(f'google/gemma-2/flax/{GEMMA_VARIANT}')

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print('GEMMA_PATH:', GEMMA_PATH)

GEMMA_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1
  1. Проверьте расположение весов модели и токенизатора, затем установите переменные пути. Каталог токенизатора будет находиться в основном каталоге, в который вы загрузили модель, а веса модели будут находиться в подкаталоге. Например:
  • Файл tokenizer.model будет находиться в /LOCAL/PATH/TO/gemma/flax/2b-it/2 ).
  • Контрольная точка модели будет находиться в /LOCAL/PATH/TO/gemma/flax/2b-it/2/2b-it ).
CKPT_PATH = os.path.join(GEMMA_PATH, GEMMA_VARIANT)
TOKENIZER_PATH = os.path.join(GEMMA_PATH, 'tokenizer.model')
print('CKPT_PATH:', CKPT_PATH)
print('TOKENIZER_PATH:', TOKENIZER_PATH)

CKPT_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/gemma2-2b-it
TOKENIZER_PATH: /root/.cache/kagglehub/models/google/gemma-2-2b/flax/gemma2-2b-it/1/tokenizer.model

Выполнить выборку/вывод

  1. Загрузите и отформатируйте контрольную точку модели Gemma с помощью метода gemma.params.load_and_format_params :
from gemma import params as params_lib

params = params_lib.load_and_format_params(CKPT_PATH)
  1. Загрузите токенизатор Gemma, созданный с помощью sentencepiece.SentencePieceProcessor :
import sentencepiece as spm

vocab = spm.SentencePieceProcessor()
vocab.Load(TOKENIZER_PATH)

True
  1. Чтобы автоматически загрузить правильную конфигурацию из контрольной точки модели Gemma, используйте gemma.transformer.TransformerConfig . Аргумент cache_size — это количество временных шагов в кеше Gemma Transformer . После этого создайте экземпляр модели Gemma как transformer с помощью gemma.transformer.Transformer (который наследуется от flax.linen.Module ).
from gemma import transformer as transformer_lib

transformer_config = transformer_lib.TransformerConfig.from_params(
    params=params,
    cache_size=1024
)

transformer = transformer_lib.Transformer(transformer_config)
  1. Создайте sampler с помощью gemma.sampler.Sampler поверх контрольной точки/весов модели Gemma и токенизатора:
from gemma import sampler as sampler_lib

sampler = sampler_lib.Sampler(
    transformer=transformer,
    vocab=vocab,
    params=params['transformer'],
)
  1. Напишите запрос в input_batch и выполните вывод. Вы можете настроить total_generation_steps (количество шагов, выполняемых при генерации ответа — в этом примере используется 100 для экономии памяти хоста).
prompt = [
    "what is JAX in 3 bullet points?",
]

reply = sampler(input_strings=prompt,
                total_generation_steps=128,
                )

for input_string, out_string in zip(prompt, reply.text):
    print(f"Prompt:\n{input_string}\nOutput:\n{out_string}")

Prompt:
what is JAX in 3 bullet points?
Output:


* **High-performance numerical computation:** JAX leverages the power of GPUs and TPUs to accelerate complex mathematical operations, making it ideal for scientific computing, machine learning, and data analysis.
* **Automatic differentiation:** JAX provides automatic differentiation capabilities, allowing you to compute gradients and optimize models efficiently. This simplifies the process of training deep learning models.
* **Functional programming:** JAX embraces functional programming principles, promoting code readability and maintainability. It offers a flexible and expressive syntax for defining and manipulating data. 


<end_of_turn>
  1. (Необязательно) Запустите эту ячейку, чтобы освободить память, если вы завершили записную книжку и хотите попробовать еще одну подсказку. После этого вы можете снова создать экземпляр sampler на шаге 3, а затем настроить и запустить приглашение на шаге 4.
del sampler

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